Sensing peripheral heuristic evidence, reinforcement, and engagement system

ABSTRACT

Systems and methods for identifying a condition associated with an individual in a home environment are provided. Sensors associated with the home environment detect data, which is captured and analyzed by a local or remote processor to identify the condition. In some instances, the sensors are configured to capture data indicative of electricity use by devices associated with the home environment, including, e.g., which devices are using electricity, what date/time electricity is used by each device, how long each device uses electricity, and/or the power source for the electricity used by each device. The processor analyzes the captured data to identify any abnormalities or anomalies, and, based upon any identified abnormalities or anomalies, the processor determines a condition (e.g., a medical condition) associated with an individual in the home environment. The processor generates and transmits a notification indicating the condition associated with the individual to a caregiver of the individual.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Application No. 17/874,010,filed Jul. 26, 2022, entitled “SENSING PERIPHERAL HEURISTIC EVIDENCE,REINFORCEMENT, AND ENGAGEMENT SYSTEM,” which is a continuationapplication of U.S. Application No. 17/574,874, filed Jan. 13, 2022,entitled “SENSING PERIPHERAL HEURISTIC EVIDENCE, REINFORCEMENT, ANDENGAGEMENT SYSTEM,” which is a continuation of U.S. Application No.17/077,785, filed Oct. 22, 2020, entitled “SENSING PERIPHERAL HEURISTICEVIDENCE, REINFORCEMENT, AND ENGAGEMENT SYSTEM,” which is a continuationof U.S. Application No. 16/169,544, filed Oct. 24, 2018, which claimspriority to and the benefit of: U.S. Application No. 62/654,975, filedApr. 9, 2018, and entitled “SENSING PERIPHERAL HEURISTIC EVIDENCE,REINFORCEMENT, AND ENGAGEMENT SYSTEM,” the entire disclosure of which ishereby incorporated herein in its entirety; and U.S. Application No.62/658,682, filed Apr. 17, 2018, and entitled “SENSING PERIPHERALHEURISTIC EVIDENCE, REINFORCEMENT, AND ENGAGEMENT SYSTEM,” the entiredisclosure of which is hereby incorporated herein in its entirety.

FIELD OF THE DISCLOSURE

The present disclosure generally relates to identifying a conditionassociated with an individual in a home environment.

BACKGROUND

As individuals age, many develop cognitive conditions or healthconditions making it difficult and/or unsafe for them to liveindependently in a home environment. However, because the signs of suchcognitive conditions and/or health conditions may be subtle, or maydevelop slowly over time, it may be difficult for caregivers todetermine whether an individual is capable of safely livingindependently.

SUMMARY

In one aspect, a computer-implemented method for identifying a conditionassociated with an individual in a home environment may be provided. Themethod may include, via one or more local or remote processors, servers,transceivers, and/or sensors: (1) capturing data detected by a pluralityof sensors associated with a home environment; (2) analyzing, by aprocessor, the captured data to identify one or more abnormalities oranomalies; and/or (3) determining, by a processor, based upon theidentified one or more abnormalities or anomalies, a conditionassociated with an individual in the home environment. The method mayadditionally include (4) generating, by a processor, to a caregiver ofthe individual, a notification indicating the condition associated withthe individual. The method may include additional, less, or alternateactions, including those discussed elsewhere herein.

In another aspect, a computer system for identifying a conditionassociated with an individual in a home environment may be provided. Thecomputer system may include one or more sensors associated with a homeenvironment, one or more processors configured to interface with the oneor more sensors, and/or one or more memories storing non-transitorycomputer executable instructions. The non-transitory computer executableinstructions, when executed by the one or more processors, cause thecomputer system to (1) capture data detected by the one or more sensors;(2) analyze the captured data to identify one or more abnormalities oranomalies; (3) determine, based upon the identified one or moreabnormalities or anomalies, a condition associated with an individual inthe home environment; and/or (4) generate, to a caregiver of theindividual, a notification indicating the condition associated with theindividual. The system may include additional, less, or alternatefunctionality, including that discussed elsewhere herein.

In still another aspect, a computer-readable storage medium havingstored thereon a set of non-transitory instructions, executable by aprocessor, for identifying a condition associated with an individual ina home environment may be provided. The instructions includeinstructions for (1) obtaining data detected by a plurality of sensorsassociated with a home environment; (2) analyzing the captured data toidentify one or more abnormalities or anomalies; (3) determining, basedupon the identified one or more abnormalities or anomalies, a conditionassociated with an individual in the home environment; and/or (4)generating, to a caregiver of the individual, a notification indicatingthe condition associated with the individual. The instructions maydirect additional, less, or alternate functionality, including thatdiscussed elsewhere herein.

In still another aspect, a computer-implemented method for training amachine learning module to identify abnormalities or anomalies in sensordata corresponding to conditions associated with individuals in homeenvironments may be provided. The computer-implemented method mayinclude (1) receiving, by a processor, historical data detected by aplurality of sensors associated with a plurality of home environments;(2) receiving, by a processor, historical data indicating conditionsassociated with individuals in each of the plurality of homeenvironments; (3) analyzing, by a processor, using a machine learningmodule, the historical data detected by the plurality of sensorsassociated with the plurality of home environments and the historicaldata indicating conditions associated with individuals in each of theplurality of home environments; and/or (4) identifying, by a processor,using the machine learning module, based upon the analysis, one or moreabnormalities or anomalies in the historical data detected by theplurality of sensors corresponding to conditions associated with theindividuals in the home environments. The method may include additional,less, or alternate actions, including those discussed elsewhere herein.

In still another aspect, a computer system for training a machinelearning module to identify abnormalities or anomalies in sensor datacorresponding to conditions associated with individuals in homeenvironments may be provided. The computer system may include one ormore processors and one or more memories storing non-transitory computerexecutable instructions. When executed by the one or more processors,the non-transitory computer executable instructions may cause thecomputer system to: (1) receive historical data detected by a pluralityof sensors associated with a plurality of home environments; (2) receivehistorical data indicating conditions associated with individuals ineach of the plurality of home environments; (3) analyze, using a machinelearning module, the historical data detected by the plurality ofsensors associated with the plurality of home environments and thehistorical data indicating conditions associated with individuals ineach of the plurality of home environments; and/or (4) identify, usingthe machine learning module, based upon the analysis, one or moreabnormalities or anomalies in the historical data detected by theplurality of sensors corresponding to conditions associated with theindividuals in the home environments. The system may include additional,less, or alternate functionality, including that discussed elsewhereherein.

In still another aspect, a computer-readable storage medium havingstored thereon a set of non-transitory instructions, executable by aprocessor, for training a machine learning module to identifyabnormalities or anomalies in sensor data corresponding to conditionsassociated with individuals in home environments may be provided. Theinstructions may include instructions for: (1) receiving historical datadetected by a plurality of sensors associated with a plurality of homeenvironments; (2) receiving historical data indicating conditionsassociated with individuals in each of the plurality of homeenvironments; (3) analyzing, using a machine learning module, thehistorical data detected by the plurality of sensors associated with theplurality of home environments and the historical data indicatingconditions associated with individuals in each of the plurality of homeenvironments; and/or (4) identifying, using the machine learning module,based upon the analysis one or more abnormalities or anomalies in thehistorical data detected by the plurality of sensors corresponding toconditions associated with the individuals in the home environments. Theinstructions may direct additional, less, or alternate functionality,including that discussed elsewhere herein.

Advantages will become more apparent to those skilled in the art fromthe following description of the preferred embodiments which have beenshown and described by way of illustration. As will be realized, thepresent embodiments may be capable of other and different embodiments,and their details are capable of modification in various respects.Accordingly, the drawings and description are to be regarded asillustrative in nature and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The Figures described below depict various aspects of the system andmethods disclosed therein. It should be understood that each Figuredepicts one embodiment of a particular aspect of the disclosed systemand methods, and that each of the Figures is intended to accord with apossible embodiment thereof. Further, wherever possible, the followingdescription refers to the reference numerals included in the followingFigures, in which features depicted in multiple Figures are designatedwith consistent reference numerals.

There are shown in the drawings arrangements which are presentlydiscussed, it being understood, however, that the present embodimentsare not limited to the precise arrangements and instrumentalities shown,wherein:

FIG. 1 illustrates an exemplary computer system for identifying acondition associated with an individual in a home environment, inaccordance with some embodiments.

FIG. 2 illustrates an exemplary home environment, in accordance withsome embodiments.

FIG. 3 illustrates several exemplary user interface displays, inaccordance with some embodiments.

FIG. 4 illustrates a flow diagram of an exemplary computer-implementedmethod for identifying a condition associated with an individual in ahome environment, in accordance with some embodiments.

FIG. 5 illustrates a flow diagram of an exemplary computer-implementedmethod for training a machine learning module to identify abnormalitiesor anomalies in sensor data corresponding to conditions associated withindividuals in home environments, in accordance with some embodiments.

The Figures depict preferred embodiments for purposes of illustrationonly. One skilled in the art will readily recognize from the followingdiscussion that alternative embodiments of the systems and methodsillustrated herein may be employed without departing from the principlesof the invention described herein.

DETAILED DESCRIPTION

As discussed above, individuals may develop cognitive conditions orhealth conditions making it difficult and/or unsafe for them to liveindependently in a home environment. However, because the signs of suchcognitive conditions and/or health conditions may be subtle, or maydevelop slowly over time, it may be difficult for caregivers todetermine whether an individual is capable of safely livingindependently.

Accordingly, systems and methods for identifying conditions associatedwith an individual in a home environment are provided herein. Sensorsassociated with the home environment may passively detect data, whichmay be captured and analyzed by a processor in order to identifypotential conditions associated with an individual in the homeenvironment. In some instances, the sensors may include sensorsconfigured to capture data indicative of electricity use by devicesassociated with the home environment, including, e.g., which devices areusing electricity, what date/time electricity is used by each device,how long each device uses electricity, and/or the power source for theelectricity used by each device. The processor may analyze the captureddata to identify any abnormalities or anomalies, and, based upon anyidentified abnormalities or anomalies, the processor may determine acondition (e.g., a medical condition, or a deviation from normalroutines or activity) associated with an individual in the homeenvironment. The processor may generate a notification indicating thecondition associated with the individual to a caregiver of theindividual. Advantageously, the caregiver may be informed of anyconditions associated with the individual that may arise.

Moreover, in some embodiments, the systems and methods for identifyingconditions associated with an individual in a home environment mayinclude systems and methods for training a machine learning module toidentify abnormalities or anomalies in sensor data corresponding toconditions associated with individuals in home environments. Forinstance, historical sensor data associated with home environments andcorresponding historically identified conditions associated withindividuals in the home environments may be used as inputs for a machinelearning module to develop a predictive model identifying which apotential condition associated with an individual in a home environmentusing abnormalities or anomalies in the sensor data associated with thehome environment.

The systems and methods provided herein therefore offer numerousbenefits. In particular, the systems and methods effectively,efficiently, and non-intrusively, identify conditions associated with anindividual in a home environment and provide a notification to acaregiver associated with the individual indicating any identifiedconditions, allowing a caregiver outside of the home environment to beinformed of the status of the individual. In this way, the safety of theindividual in the home environment may be improved. That is, because thecaregiver will be alerted of conditions affecting the individual, whichmay include health conditions, cognitive conditions, etc., the caregivermay provide any assistance needed to the individual. Moreover, thecaregiver outside the home environment may be able to use theinformation provided to continually assess whether the individual maycontinue to safely live independently in the home environment, andaccordingly make changes in the individual’s care when needed.

Furthermore, according to certain implementations, the systems andmethods may support a dynamic, real-time or near-real-time analysis ofany captured, received, and/or detected data. In particular, in someembodiments, a caregiver device may receive an indication of a conditionassociated with the individual in the home environment in real-time ornear real-time, and may automatically and dynamically take actions suchas requesting emergency medical assistance when needed. In this regard,any caregiver is afforded the benefit of accurate and relevant data, andmay provide immediate care and assistance as needed.

Exemplary Computer System

Turning to FIG. 1 , an exemplary computer system 100 for identifying acondition associated with an individual in a home environment isillustrated. The high-level architecture illustrated in FIG. 1 mayinclude both hardware and software applications, as well as various datacommunications channels for communicating data between the varioushardware and software components, as is described below.

The computer system 100 may include one or more sensors 102 associatedwith a home environment 104 such as, e.g., a house, apartment,condominium, or other living space associated with an individual. Thecomputer system 100 further includes a server 106, and a caregiverdevice 108, each of which may communicate with one another (and/or withthe sensors 102) using one or more network 110, which may be a wirelessnetwork, or which may include a combination of wireless and wirednetworks.

The sensors 102 may include, for example, electrical use sensorsconfigured to detect data indicating, e.g., which outlets within thehome environment are using electricity, which devices within the homeenvironment are using electricity, the amount of electricity used byeach device, dates and/or times at which each device uses electricity,the duration of time for which each device uses electricity, the powersource for each device’s electricity use (e.g., standard power sourcesversus generator in emergency situation), etc. Devices within the homeenvironment may include appliances (e.g., stoves (including ignitionsources for gas stoves), ovens, dishwashers, washers, dryers,refrigerators, freezers, microwaves, etc.), electronic devices (e.g.,televisions, telephones, computers, etc.), heating and cooling devices(e.g., HVAC unit, furnace, hot water heater, etc.), lighting devices(e.g., overhead lights, lamps, etc.), water pumps, garage door openers,alarm systems, exercise equipment (e.g., treadmills, stationary bikes,elliptical machines, etc.), etc.

Additionally, in some instances, the sensors 102 associated with thehome environment may also include smart sensors, or sensors associatedwith smart phones or other “smart” devices (i.e., devices connected tocellular and/or internet networks, or otherwise able to communicate withother devices, e.g., using Bluetooth) within the home environment, suchas, e.g., virtual assistant devices, smart home devices, smartthermostats, smart appliances (e.g., smart ovens, smart dishwashers,smart washers, smart dryers, smart refrigerators, smart freezers, smartmicrowaves, etc.), smart lighting systems, smart speaker systems, smartrobotics (e.g., smart robotic vacuums), smart motion sensors, smartwater sensors, smart gas and ignition monitors, smart contact sensors,smart air movement and/or draft sensors, smart HVAC systems, smart petmonitor devices, smart medication dispensers, smart wearableelectronics, smart alarm systems or security systems, smart scales, orany other suitable smart devices.

Moreover, in some instances, additional data may be captured by othersuitable sensors 102 or other devices associated with the homeenvironment (not shown), e.g., geo-locator tags associated with the homeenvironment, weather monitoring devices associated with the homeenvironment, vehicle sensors associated with the home environment (orwith an individual within the home environment), wireless internet usagemonitors, 3D printers, nanobots, fine motor control measurement devices,or any other suitable sensors or devices.

The sensors 102 may be configured to detect many different types ofdata, including but not limited to video data, image data, audio and/ordecibel data, activity and/or movement data, vibration data, light data,arm/disarm data (i.e., with respect to an alarm system), bodytemperature data associated with the individual, home environmenttemperature data, moisture data, odor data, heart rate data associatedwith the individual, breathing rate data associated with the individual,hydration data associated with the individual, weight data associatedwith the individual, glucose/ketones levels associated with theindividual, medication adherence data, travel and/or location dataassociated with the individual, socialization data associated with theindividual, medical/health monitor device use data associated with theindividual, appliance use data associated with the individual,electronics use data associated with the individual, air quality data,air quality data, sleep data associated with the individual, eyemovement data associated with the individual, exercise data associatedwith the individual, body control data associated with the individual,fine motor control data associated with the individual, speech dataassociated with the individual, health and/or nutrition data associatedwith the individual, hygiene data associated with the individual, sightand/or hearing data associated with the individual, etc. Accordingly,this data may be communicated to the server 106 and/or caregiver device108 via the network 110.

The server 106 may in some instances be a collection of multipleco-located or geographically distributed servers, etc. Additionally,although only one server 106 is shown in FIG. 1 , there may be manyservers 106. Furthermore, the server may include a processor 112 and amemory 114. The processor 112 may in some embodiments include multipleprocessors, and may be configured to execute any software applicationsresiding on the memory 114. The software applications may be configuredto analyze the data detected by the sensors 102 to determine informationassociated with individuals in the home environment 104.

For example, by analyzing the data detected by the sensors 102, theserver 106 may determine indications of, e.g., presence (in particularlocations or in the home environment generally), walking patterns,falls, hazards, imminent danger, evidence of atypical behavior(physical, mental, emotional, social), intruders in the homeenvironment, theft in the home environment, fraud, abuse, position offurniture (e.g., for safety layout recommendations), trips, falls andother hazards, moisture and/or puddles on the floor or other surfaces orceiling, whether lights are on or off, typical and atypical voicepatterns (e.g., representing stoke, Alzheimer’s detection, hearingdecline, cognitive decline), socialization (such as decline inconversation or detecting other people in the house), falls, behavioralchange (e.g., more aggression, more arguing, less conversation thanusual) laughter, crying, other sounds relating to emotion, vehiclecoming and going, movements of the individual from room to room, paceand speed of movement of the individual, duration of time spent in aspace by the individual, time the individual spends outside of thehouse, atypical movements (e.g., seizures, movements before or after afall or injury) walking patterns and/or gaits, movements and activitiesrelated to cooking, cleaning, exercise, entertainment, socialization,movements associated with a trip or stumble, eye movement, fall andimpact level of fall, items dropping or breaking, entrance and or exitof the individual into or out of the home environment, tornados,earthquakes, other disaster events, phone calls and/or texts, vehiclescoming and/or going, locations in the home environment where lights areturned on or off, for time duration of lights turned on and/or off, dateof activation, activity of automatic lights that activate when movementor other stimulus is detected, alarm activation and/or disarm, alarmactivation with no disarm, number of times over an amount of time, suchas hour or single day an alarm is armed and disarmed, frequency oramount of accidental alarm activations, exit times when alarm is armed,home environment temperature (e.g., highs, lows, averages), hot watertemperature from heater, faucets, and bathtub/showers, oven or stovetoptemperatures, body temperature of inhabitants, differentiation oftemperature between rooms of the house, opening and/or closing of vents,plumbing leaks, sump pump activation/issue, humidity averages and out ofaverage range in the home environment, bed wetting or accidents, carbonmonoxide, carbon dioxide, air quality, smoke, stagnant air, mold/mildew,ammonia, body odor, feces, pet, urine, natural gas, burning food,presence of certain foods, medical information associated with theindividual (such as heart rate, blood pressure, cholesterol, glucose,ketones, weight, hydration, nutrition, medication adherence, medicaldevice use or adherence, breathing rate), GPS location, travelitinerary, routine locations traveled to, mode of travel (e.g., plane,train, automobile, ride sharing services), travel services, duration oftravel, travel delays, travel interruptions or difficulties, interactionroutines, individuals with whom the individual frequently interacts,frequency of interactions, types of interactions, internet usage oractivity, streaming television shows or movies, social media interactionor activity, social media postings, email usage, etc.

Moreover, the memory 114 may include multiple memories, which may beimplemented as semiconductor memories, magnetically readable memories,optically readable memories, biologically readable memories, and/or anyother suitable type(s) of non-transitory, computer-readable storagemedia. Additionally, the server may be configured to access a database116 (e.g., via the network 110), which may store data related to, interalia, behavioral patterns of individuals in the home environment 104,conditions associated with individuals in the home environment 104, orother suitable information associated with individuals in the homeenvironment 104, etc. In some instances, blockchain encryption may beimplemented to securely store this data. The data stored by the database116 may be used by any software applications stored in the memory 114.

For example, various software applications stored in the memory 114 maybe configured to analyze the information associated with the individualto identify abnormalities or anomalies associated with the individual(e.g., abnormalities or anomalies in the individual’s behaviors). Basedupon the identified abnormalities or anomalies, a condition associatedwith an individual in the home environment may be determined.Specifically, the condition (e.g., a medical condition, cognitivecondition, etc.) may be determined based upon atypical behaviors of theindividual indicated by the identified abnormalities or anomalies. Basedupon the condition, the software applications may be configured togenerate a notification for a caregiver of the individual, and/ortransmit the notification to a caregiver device 108.

The caregiver device 108 may be, for instance, a personal computer,cellular phone, smart phone, tablet computer, smart watch, a wearableelectronic device such as a fitness tracker, a dedicated caregiverdevice, or any other suitable mobile device associated with a caregiverof an individual in the home environment 104. Furthermore, the caregiverdevice 108 may include a processor 118 and a memory 120. The processor118 may in some embodiments include multiple processors, and may beconfigured to execute any software applications residing on the memory120. Moreover, the memory 120 may include multiple memories, which maybe implemented as semiconductor memories, magnetically readablememories, optically readable memories, biologically readable memories,and/or any other suitable type(s) of non-transitory, computer-readablestorage media. Additionally, the caregiver device 108 may include a userinterface 122 (e.g., a display configured to display a user interface),upon which notifications, alerts, etc. may be displayed, as discussed ingreater detail with respect to FIG. 3 below.

Exemplary Home Environment

Turning now to FIG. 2 , an exemplary home environment 200 isillustrated, in accordance with some embodiments. An individual 202 inthe home environment 200 may use devices within the home environment 200as part of a daily routine. For example, the individual 202 may wake upevery morning and switch on a lamp 204 before using a shower 206 (and/orsink 208, toilet 210, or bath 212). Next, the individual 202 may switchon a television 214, check emails or social media posting, and/or aliving room lamp 216, before making breakfast using a stove 218 or oven220.

Data captured by electrical use sensors (or other sensors in the homeenvironment) 102 indicating the typical usage of each of these deviceswithin the home environment 200 may be utilized to establish patternsand/or routines associated with the individual 202. For example, datacaptured by electrical use sensors indicating that the lamp 204 useselectricity starting every morning at 6:00 a.m. may indicate that theindividual 202 has a pattern or routine of waking every morning at 6:00a.m. As another example, data captured by electrical use sensorsindicating that the television 214 uses energy for 2-3 hours per day mayindicate that the individual 202 has a pattern or routine of watching2-3 hours of television per day.

Of course, other of the sensors 102 may also be utilized to establishpatterns and/or routines associated with the individual 202 in variousembodiments. For instance, data stored by smart lighting may indicateusage times of various lamps within the home environment, data stored bya smart television may indicate the duration of use of the television,data stored by a laptop or other computer may indicate usage times ofthe internet or streaming television shows, internet usage may alsoindicate social media usage or time-of-day usage, etc.

By analyzing the captured data, information about the behaviors of theindividual within the home environment may be determined. Specifically,the captured data may be analyzed to identify abnormalities oranomalies. Based upon the identified abnormalities or anomalies, acondition associated with an individual in the home environment may bedetermined. Specifically, the condition (in some instances, a medicalcondition) may be determined based upon atypical behaviors of theindividual indicated by the identified abnormalities, abnormalities, orshifts in behavior patterns. Consequently, a notification indicating thecondition associated with the individual 202 may be generated and/ordisplayed for a caregiver of the individual (such as a parent, child,spouse, doctor, nurse, or other medical caregiver, assisted livingfacility caregiver or other professional caregiver, etc.), e.g., via acaregiver device 108.

Exemplary User Interface

FIG. 3 illustrates several exemplary user interface displays 300, 302,304 generated and displayed for a caregiver (e.g., via a caregiverdevice 108) of an individual in a home environment based upon the datacaptured by the sensors 102. For example, user interface display 300displays an alert indicating that a medical emergency associated withthe individual in the home environment has been detected. Consequently,the caregiver may be presented with options to call the individual,and/or to call an emergency service (e.g., medical services, fireservices, police services, etc.)

As another example, user interface display 302 displays an updateindicating that a snapshot report for June 2018 is available forcaregiver review. User interface display 304 displays an exemplary June2018 Snapshot report associated with the individual in the homeenvironment, generated for the caregiver of the individual.

In various embodiments, snapshot reports as shown in user interfacedisplay 304 may be generated periodically (e.g., daily, weekly, and/oryearly), showing the individual’s progress or decline in various areas,as well as items of potential concern. In some instances, the snapshotreport may further include an indication of, e.g., whether theindividual is able to live independently, whether the individual mayneed assistance, whether a fall or injury may have occurred, whether theindividual is bathing, eating, or sleeping, potential cognitive ormedical issues, whether the individual is taking his or her medicationand/or whether the medication is helping the individual, etc. Forexample, as shown in exemplary user interface display 304, the snapshotreport includes a notification that early signs of Alzheimer’s diseasehave been detected in the individual, and that a possible fall has beendetected.

In various embodiments the report may include one or more of customizedtime period reports, comparative timeframe reports, an indication of thenumber of notifications sent to caregiver and for what, medicationtaken, for what, and if that medication is working, areas of concern,areas of decline, areas where intervention or improvement needs to takeplace, areas where the individual needs assistance or additionalassistance, suggested and/or recommend actions to be taken by anindividual or by a caregiver, recommended services, support, andresources within a certain proximity of the location, number ranges ofoptimal (based upon age/ability) levels of physical, mental, social, andemotional engagement, activity, and/or ability, goals and/or goalsetting/meeting features, etc. In some instances, the report may includean independent living overall score (e.g., with 100 representing idealindependent living capability, 70 representing family/support servicesinteraction needed for independent living capability, 60 representingsignificant family/support services interaction needed to liveindependently, 50 and below representing professional assistance needed,etc.). For example, as shown in exemplary user interface display 304,the snapshot report includes an independent living score of 50.

Exemplary Computer-Implemented Method for Identifying ConditionAssociated With Individual in Home Environment

Turning now to FIG. 4 , a flow diagram of an exemplarycomputer-implemented method 400 for identifying a condition associatedwith an individual in a home environment is illustrated, in accordancewith some embodiments. The method 400 can be implemented as a set ofinstructions stored on a computer-readable memory and executable on oneor more processors.

At block 402, data detected by a plurality of sensors (e.g., sensors102) associated with a home environment (e.g., home environment 104) iscaptured. For example, an electrical use sensor may detect that, at agiven time, an oven in the home environment is currently usingelectricity. As another example, an electrical use sensor may detectdata indicating that a lamp in the home environment uses power onweekdays starting at 6:00 a.m., and on weekends starting at 8:00 a.m. Asstill another example, an electrical use sensor may detect dataindicating that a water pump in the home environment has been operatingfor a certain amount of time (e.g., 15 minutes, one hour, all day,etc.). As an additional example, an electrical use sensor may detectthat a furnace in the home environment is using power from a powergenerator.

By analyzing the captured data, information about the behaviors of theindividual within the home environment may be determined. In someinstances, the information indicated by the captured data may relate tocurrent behaviors of the individual. For example, data from anelectrical use sensor indicating that an oven in the home environment iscurrently using electricity may indicate that an individual in the homeenvironment is currently cooking a meal. As another example, from anelectrical use sensor indicating that a water pump in the homeenvironment has been operating for a certain amount of time (e.g., 15minutes, one hour, all day, etc.) may indicate that an individual withinthe home environment is currently taking a shower or a bath.

Additionally, over time, the information indicated by the captured datamay be used to determine patterns in the behaviors of the individual. Inother words, the captured data may be analyzed to identify data patternsindicative of behavior patterns. For instance, data from an electricaluse sensor indicating that a lamp in the home environment uses powerevery day starting at 6:00 a.m. may indicate that an individual withinthe home environment wakes up around 6:00 a.m. each morning. Similarly,data from an electrical use sensor indicating that a water pump in thehome environment typically operates for 15 minutes at 6:30 a.m. mayindicate that the individual typically showers at 6:30 a.m. eachmorning.

At block 404, the captured data is analyzed to identify abnormalities oranomalies. The captured data may be compared against identified patternsin order to identify instances in which the data is inconsistent withthe identified patterns, which may in turn indicate abnormalities oranomalies in the behaviors of the individual. For instance, in theexample described above, an identified pattern in the electrical usageof a lamp in the home environment starting at 6:00 a.m. may indicate atypical wake-up time of 6:00 a.m. for an individual in the homeenvironment. However, if the captured data indicates that one day thelamp does not use electricity until 8:00 a.m., an anomaly or abnormalitymay be identified for that day. This captured data may in turn indicatean anomaly or abnormality in the behavior of the individual, i.e., thatthe individual woke up later than usual on that day.

As another example, data from an electrical use sensor may indicate thata water pump in the home environment operates once per day for 15minutes, which may in turn indicate that the user showers every day.However, if the captured data indicates that one day the water pump doesnot operate, or operates for a time duration shorter than a typicalshower (e.g., only operates for 2 minutes), an abnormality or anomalymay be identified for that day. This captured data may in turn indicatean abnormality or anomaly in the behavior of the individual, i.e., thatthe individual did not shower on that day.

In some instances, analyzing the captured data to identify abnormalitiesor anomalies may include analyzing the captured data to identify theemergence of new behavior patterns, or shifts in behavior patterns ofthe individual. For instance, using the example described above, overseveral years, an identified pattern in the electrical usage of a lamp(or television or computer) in the home environment starting at 6:00a.m. may indicate a typical wake-up time of 6:00 a.m. for an individualin the home environment over those several years. However, over the mostrecent month, a new pattern in the electrical usage of the lamp mayemerge, indicating a new typical wake-up time of 8:00 a.m. for theindividual in the home environment.

As another example, a pattern in the electrical usage of a stove or ovenmay indicate that an individual in the home environment uses the stoveor oven (likely to cook a meal) five times per week over the course ofsix months. However, over the most recent six months, a new pattern inthe electrical usage of the stove or oven may emerge, indicating thatthe individual in the home environment uses the stove or oven (likely tocook a meal) only one time per week.

At block 406, a condition associated with an individual in the homeenvironment is determined based upon the identified abnormalities oranomalies. Specifically, the condition (in some instances, a medicalcondition) may be determined based upon atypical behaviors of theindividual indicated by the identified abnormalities, abnormalities, orshifts in behavior patterns. For example, if the captured data indicatesthat a user has left a stove or oven on for an atypically long time, aforgetfulness condition associated with the individual may be determined(i.e., because the user likely forgot that the stove or oven was on).

As another example, if the captured data previously indicated that theindividual likely showered once per day, and the capture data indicatesthat the individual currently showers only once per week, a hygienecondition associated with the individual may be determined (i.e.,because the individual is likely bathing less frequently). As stillanother example, if the captured data indicates that the individualwakes up at a time that is atypically late, an insomnia or fatiguecondition associated with the individual may be determined.

Other atypical behaviors that may be detected based upon identifiedabnormalities or anomalies may include, for instance, lights not beingturned on/off, changes in laundry habits, refrigerator not opening andclosing, alarm not armed or disarmed, slurred speech when using a voiceassistant or passive listening, change in gait, water running too long(i.e., suggesting a shower fall or cognitive failure), sensing a largemass has quickly flattened (i.e., suggesting a fall has occurred).

At block 408, a notification indicating the condition associated withthe individual is generated. The notification may be displayed to acaregiver of the individual, such as a parent, child, spouse, doctor,nurse, or other medical caregiver, assisted living facility caregiver orother professional caregiver, etc. In some instances, the notificationmay indicate a specific event, such as a fall, an entrance or an exit, atravel event, a medical event, or another critical event associated withthe individual.

In some examples, when the condition associated with the individual isan emergency medical condition or other urgent condition, a request foremergency services to be provided to the individual may be generated.For instance, ambulance, police, or fire services may be requested.

In some instances, e.g., as shown in FIG. 3 , the notification may be adigital “snapshot” report generated periodically (e.g., daily, weekly,and/or yearly), showing the individual’s progress or decline in variousareas, as well as items of potential concern. The report may furtherinclude an indication of, e.g., whether the individual is able to liveindependently, whether the individual may need assistance, whether afall or injury may have occurred, whether the individual is bathing,eating, or sleeping, potential cognitive issues, whether the individualis taking his or her medication and/or whether the medication is helpingthe individual, etc. In some instances, the reports may be configured tobe shared with medical professionals, family members, caregivers, etc.Blockchain encryption may be implemented in some instances to keep thereport information secure.

Exemplary Computer-Implemented Method for Training Machine LEARNINGMODULE

Turning now to FIG. 5 , a flow diagram of an exemplarycomputer-implemented method 500 for training a machine learning moduleto identify abnormalities or anomalies in sensor data corresponding toconditions associated with individuals in home environments isillustrated, in accordance with some embodiments. The method 500 can beimplemented as a set of instructions stored on a computer-readablememory and executable on one or more processors.

At block 502, historical data detected by a plurality of sensors (e.g.,sensors 102) associated with a plurality of home environments (e.g.,home environment 104) may be received. In some instances, the historicalsensor data may be received upon accessing a database (e.g., database116). Similarly, at block 504, historical data indicating one or moreconditions associated with the individuals in each home environment maybe received. In some instances, the historical data may be received uponaccessing a database (e.g., database 116).

At block 506, the historical data detected by the plurality of sensorsassociated with the plurality of home environments and the historicaldata indicating conditions associated with individuals in each homeenvironment may be analyzed using a machine learning module (orartificial intelligence, or other machine learning models, programs, oralgorithms). That is, the historical sensor data and/or the historicalcondition data may be used as input for the machine learning module,which may employ a neural network, which may be a convolutional neuralnetwork, a deep learning neural network, or a combined learning moduleor program that learns in two or more fields or areas of interest.Re-inforced or reinforcement learning techniques may also be used.

The machine learning module’s analysis of the historical sensor data andthe historical condition data may include identifying and recognizingpatterns in the data, including the types of data and usage datadiscussed herein. In some embodiments, existing data, including usage,text or voice/speech data may be used in order to facilitate makingpredictions for subsequent data. Voice recognition and/or wordrecognition techniques may also be used. Models may be created basedupon example inputs in order to make valid and reliable predictions fornovel inputs.

Additionally or alternatively, the machine learning programs may betrained by inputting sample data sets or certain data into the programs,such as mobile device, smart home, smart home sensor, drone, autonomousor semi-autonomous drone, image, vehicle telematics, smart or autonomousvehicle, and/or intelligent home telematics data. In general, trainingthe neural network model may include establishing a networkarchitecture, or topology, and adding layers that may be associated withone or more activation functions (e.g., a rectified linear unit,softmax, etc.), loss functions and/or optimization functions. Data setsused to train the artificial neural network(s) may be divided intotraining, validation, and testing subsets; these subsets may be encodedin an N-dimensional tensor, array, matrix, or other suitable datastructures.

Training may be performed by iteratively training the network usinglabeled training samples. Training of the artificial neural network mayproduce byproduct weights, or parameters which may be initialized torandom values. The weights may be modified as the network is iterativelytrained, by using one of several gradient descent algorithms, to reduceloss and to cause the values output by the network to converge toexpected, or “learned”, values. In an embodiment, a regression neuralnetwork may be selected which lacks an activation function, whereininput data may be normalized by mean centering, to determine loss andquantify the accuracy of outputs. Such normalization may use a meansquared error loss function and mean absolute error. The artificialneural network model may be validated and cross-validated using standardtechniques such as hold-out, K-fold, etc. In some embodiments, multipleartificial neural networks may be separately trained and operated,and/or separately trained and operated in conjunction.

Furthermore, the machine learning programs may utilize deep learningalgorithms that may be primarily focused on pattern recognition, and maybe trained after processing multiple examples. The machine learningprograms may include Bayesian program learning (BPL), voice recognitionand synthesis, image or object recognition, optical characterrecognition, and/or natural language processing - either individually orin combination. The machine learning programs may also include naturallanguage processing, semantic analysis, automatic reasoning, and/orother machine learning techniques, including those discussed elsewhereherein.

In supervised machine learning, a processing element may be providedwith example inputs and their associated outputs, and may seek todiscover a general rule that maps inputs to outputs, so that whensubsequent novel inputs are provided the processing element may, basedupon the discovered rule, accurately predict the correct output. Inunsupervised machine learning, the processing element may be required tofind its own structure in unlabeled example inputs.

At block 508, one or more abnormalities or anomalies in the historicaldata detected by the plurality of sensors corresponding to theconditions associated with the individuals in the home environments maybe identified using a machine learning module, based upon the analysis.The identified abnormalities or anomalies in the historical data andtheir corresponding conditions may comprise a predictive model to beused to analyze current sensor data. For example, the model may includea prediction of a condition associated with an individual in the homeenvironment based upon certain abnormal or anomalous patterns in currentsensor data.

In some instances, certain aspects of the exemplary computer-implementedmethod 500 may be combined with aspects of the exemplarycomputer-implemented method 400. For example, the identifiedabnormalities or anomalies in the historical data and theircorresponding conditions discussed with respect to block 508 may beutilized in the analysis discussed with respect to block 404 of themethod 400, e.g., by comparing the one or more abnormalities oranomalies in current sensor data to the identified abnormalities oranomalies in the historical data and their corresponding conditions. Ofcourse, additional or alternative combinations of these methods may beenvisioned in various embodiments.

Exemplary Systems & Methods for Anomaly Detection

In one aspect, a computer-implemented method for identifying a conditionassociated with an individual in a home environment may be provided. Themethod may include (1) capturing or receiving data detected by aplurality of sensors associated with a home environment, such as at oneor more transceivers associated with one or more local or remoteprocessors via wireless transmission or data transmission over one ormore radio frequency links; (2) analyzing, by the one or more local orremote processors, the captured or received sensor data to identify oneor more abnormalities or anomalies; (3) determining, by the one or morelocal or remote processors, based upon the identified one or moreabnormalities or anomalies, a condition associated with an individual inthe home environment; (4) generating, by the one or more local or remoteprocessors a notification indicating the condition associated with theindividual; and/or (5) transmitting, by the one or more local or remoteprocessors and/or associated transceivers the notification to acaregiver mobile device, the caregiver being associated with theindividual. The method may include additional, less, or alternateactions, including those discussed elsewhere herein.

In another aspect, a computer system for identifying a conditionassociated with an individual in a home environment may be provided. Thesystem may include one or more sensors associated with a homeenvironment; one or more local or remote processors and/or associatedtransceivers configured to interface with the one or more sensors; andone or more memories storing non-transitory computer executableinstructions that, when executed by the one or more processors, causethe computer system to: (1) capture data detected by the one or moresensors, or receive data generated by the one or more sensors, such asdata wirelessly communicated over one or more radio frequency links; (2)analyze the captured data to identify one or more abnormalities oranomalies; (3) determine, based upon the identified one or moreabnormalities or anomalies, a condition associated with an individual inthe home environment; (4) generate an electronic notification indicatingthe condition associated with the individual; and/or (5) transmit theelectronic notification, such as via wireless communication or datatransmission over one or more radio frequency links to a mobile deviceor other computing device associated with a caregiver associated withthe individual. The system may include additional, less, or alternatefunctionality, including that discussed elsewhere herein.

In another aspect, a computer-implemented method for training a machinelearning module to identify abnormalities or anomalies in sensor datacorresponding to conditions associated with individuals in homeenvironments may be provided. The method may include (1) receiving, byone or more local or remote processors, historical data detected by aplurality of sensors associated with a plurality of home environments;(2) receiving, by the one or more local or remote processors, historicaldata indicating conditions associated with individuals in each of theplurality of home environments; (3) analyzing, by the one or more localor remote processors, using a machine learning module, (i) thehistorical data detected by the plurality of sensors associated with theplurality of home environments, and/or (ii) the historical dataindicating conditions associated with individuals in each of theplurality of home environments; and/or (4) identifying, by the one ormore local or remote processors, using the machine learning module,based upon the analysis, one or more abnormalities or anomalies in thehistorical data detected by the plurality of sensors corresponding toconditions associated with the individuals in the home environments. Themethod may also include (5) capturing or receiving current data detectedby a plurality of sensors associated with a home environment; (6)analyzing, by the one or more local or remote processors and/or thetrained machine learning module, the captured current data to identifyone or more abnormalities or anomalies in the current data; (7)comparing, by the one or more local or remote processors and/or thetrained machine learning module, the one or more abnormalities oranomalies in the current data to the abnormalities or anomalies in thehistorical data detected by the plurality of sensors corresponding toconditions associated with the individuals in the home environments;and/or (8) determining, by the one or more local or remote processorsand/or the trained machine learning module, based upon the comparison, acurrent condition associated with an individual in the home environment.The method may include additional, less, or alternate actions, includingthose discussed elsewhere herein.

In one aspect, a computer system for training a machine learning moduleto identify abnormalities or anomalies in sensor data corresponding toconditions associated with individuals in home environments may beprovided. The system may include one or more local or remote processorsand/or associated transceivers; and one or more memories storingnon-transitory computer executable instructions that, when executed bythe one or more processors and/or associated transceivers, cause thecomputer system to: (1) receive historical data detected by a pluralityof sensors associated with a plurality of home environments; (2) receivehistorical data indicating conditions associated with individuals ineach of the plurality of home environments; (3) analyze, using a machinelearning module, (i) the historical data detected by the plurality ofsensors associated with the plurality of home environments and/or (ii)the historical data indicating conditions associated with individuals ineach of the plurality of home environments; and/or (4) identify, usingthe machine learning module, based upon the analysis, one or moreabnormalities or anomalies in the historical data detected by theplurality of sensors corresponding to conditions associated with theindividuals in the home environments. The system may include additional,less, or alternate functionality, including that discussed elsewhereherein.

In one aspect, a computer-implemented method for training a machinelearning module to identify abnormalities or anomalies in sensor datacorresponding to conditions associated with individuals in homeenvironments may be provided. The method may include (1) receiving, byone or more local or remote processors and/or associated transceivers(such as via wireless communication or data transmission over one ormore radio frequency links or communication channels), historical datadetected by a plurality of sensors associated with a plurality of homeenvironments; (2) receiving, by the one or more local or remoteprocessors and/or associated transceivers, historical data indicatingconditions associated with individuals in each of the plurality of homeenvironments; (3) inputting, by the one or more local or remoteprocessors, into a machine learning module (i) the historical datadetected by the plurality of sensors associated with the plurality ofhome environments and/or (ii) the historical data indicating conditionsassociated with individuals in each of the plurality of homeenvironments to train the machine learning module to identify one ormore abnormalities or anomalies in the historical data detected by theplurality of sensors corresponding to conditions associated with theindividuals in the home environments; (4) capturing current datadetected by a plurality of sensors associated with a home environment,or receiving current data detected by the plurality of sensorsassociated with the home environment, via the one or more local orremote processors and/or associated transceivers; (5) inputting, by theone or more local or remote processors, the current data into thetrained machine learning module to identify one or more abnormalities oranomalies in the current data and/or an individual in the current data;(6) generating, by the one or more local or remote processors, anotification regarding the one or more abnormalities or anomalies and/orthe individual; and/or (7) transmitting, by the one or more local orremote processors and/or transceivers, the notification to a mobile orother computing device of a caregiver for the individual. The method mayinclude additional, less, or alternate actions, including thosediscussed elsewhere herein.

In another aspect, a computer system configured to train a machinelearning module to identify abnormalities or anomalies in sensor datacorresponding to conditions associated with individuals in homeenvironments may be provided. The system may include one or more localor remote processors, memories, sensors, transceivers, and/or serversconfigured to: (1) receive, such as via wireless communication or datatransmission over one or more radio frequency links or communicationchannels, historical data detected by a plurality of sensors associatedwith a plurality of home environments; (2) receive, such as via wirelesscommunication or data transmission over one or more radio frequencylinks or communication channels, historical data indicating conditionsassociated with individuals in each of the plurality of homeenvironments; (3) input, into a machine learning module, (i) thehistorical data detected by the plurality of sensors associated with theplurality of home environments and/or (ii) the historical dataindicating conditions associated with individuals in each of theplurality of home environments to train the machine learning module toidentify one or more abnormalities or anomalies (and/or conditionsassociated with an individual) in the historical data detected by theplurality of sensors corresponding to conditions associated with theindividuals in the home environments; (4) capture current data detectedby a plurality of sensors associated with a home environment, orreceive, such as via wireless communication or data transmission overone or more radio frequency links or communication channels, currentdata detected by the plurality of sensors associated with the homeenvironment; (5) input the current data into the trained machinelearning module to identify one or more abnormalities or anomalies inthe current data and/or an individual in the current data; (6) generatean electronic notification regarding the one or more abnormalities oranomalies and/or the individual; and/or (7) transmit the electronicnotification, such as via wireless communication or data transmissionover one or more radio frequency links or communication channels, to amobile or other computing device of a caregiver for the individual. Thesystem may include additional, less, or alternate functionality,including that discussed elsewhere herein.

Other Matters

The computer-implemented methods discussed herein may includeadditional, less, or alternate actions, including those discussedelsewhere herein. The methods may be implemented via one or more localor remote processors, transceivers, servers, and/or sensors (such asprocessors, transceivers, servers, and/or sensors mounted on homes,mobile devices, vehicles, computers, televisions, drones, or associatedwith smart infrastructure or remote servers), and/or viacomputer-executable instructions stored on non-transitorycomputer-readable media or medium.

Additionally, the computer systems discussed herein may includeadditional, less, or alternate functionality, including that discussedelsewhere herein. The computer systems discussed herein may include orbe implemented via computer-executable instructions stored onnon-transitory computer-readable media or medium.

In some embodiments, a processor or a processing element may be trainedusing supervised or unsupervised machine learning, and the machinelearning program may employ a neural network, which may be aconvolutional neural network, a deep learning neural network, or acombined learning module or program that learns in two or more fields orareas of interest.

Exemplary Spheres Embodiments

In one embodiment, a SPHERES system may be provided. SPHERES - orSensing Peripheral Heuristic Evidence, Reinforcement, and EngagementSystem - may be a network of passive smart systems (i.e., Electric usemonitoring sensors, connected home assistant, connected smart homesystems, connected video security and more) combined with AI/software toform a solution which autonomously and passively monitors and measuresspheres of information, behaviors, and data points, looking for routinesand changes to those routines. In one embodiment, the sensor or otherdata collected may provide actionable insights to caregivers based upondiscrete data points, and may monitor routines of certain individuals atrisk of medical or other issues.

From passive data collection, SPHERES enables live tracking andnotification of the routine norms and abnormalities/anomalies, impactsand dangers within the data and provides instant digital notificationwhen the system senses a change which could cause harm, injury, or loss,is an emergency, or needs immediate additional human interaction andfollow-up.

The system also produces on-demand, daily, monthly, yearly, and customtime period-over-time period digital snapshots of collected data toeasily understand changes (and red flags) in norms, routines, andbehaviors. SPHERES reports include recommendations based upon thecollected data points to improve user experience and engagement,increase safety or efficiency, decrease time to intervention/action,save money, or otherwise decrease negative aspects or improve positiveimpacts. To keep all this personal data safe, it is stored usingBlockchain encryption.

One exemplary embodiment may be related to independent/senior living.Here, SPHERES, using electric monitoring sensors, connected smart homedevices and assistants (such as Nest, Google Home, Amazon Alexa), andsecurity devices (such as Canary and ADT), and a PERS device (such asLife Alert), a family caregiver could understand the daily routine of asemi-independent loved one, know instantly or within a short period oftime, if there are red flags or emergencies which need interaction, andover time, have information they need to help the care recipient makeinformed independent living or enhanced caregiving environment choices.

In this example, SPHERES would monitor energy use within the home, suchas when lights are turned on or off, when computers are activated, whenwater pumps are running, and when large appliances or TVs and devicesare running, when a smart light activates or senses presence, and/orwhen a garage or other door open. SPHERES may also monitor securityfeatures such as cameras and alarm systems, and/or monitor homeassistants, such as a voice-activated assistant.

SPHERES, using passive monitoring, keeps track of an independent livingconsumer’s daily routine. Because humans are creatures of habit,trends/routines - such as sleep and wake time, breakfast, lunch, anddinner routines, entertainment themes, enter/exit themes, movementaround house or property patterns, personal hygiene themes, etc. - maybe formed. Additionally, everyone has their own walking pattern, speechpattern, and other patterns which are unique.

By using a connected network of smart appliances, devices, and systems,sensor technology, and AI/software data sorting and analysis, SPHEREScan understand when something is typical or atypical, such as: lightsnot being turned on/off; changes in laundry habits; refrigerator notopening and closing; alarm not armed or disarmed; when speech is slurredwhen using a voice assistant or passive listening; a camera could sensea person’s gate has changed maybe due to injury; water running too longcould suggest a shower fall or cognitive failure; sensing a large masssimilar to that of the resident has quickly flattened suggesting a fallhas occurred; and/or many other data points and abnormalities in routine(including those mentioned elsewhere herein) suggesting action andcaregiver instant notification is needed.

SPHERES may also include reporting, recommendations, and action planfunctionality. Using multiple data points over time, digital reports maybe generated showing progress or decline and items of concern. Thesesnapshot reports may be used to understand the whole picture of a lovedone’s Independent Living situation. Are they able to live independently?Do they need assistance? Has a fall or injury happened? Are theydepressed and staying in bed longer/not eating? Are there cognitiveissues? Is medication being taken? Is the medication beneficial oraffective? Based upon passive monitoring of routines and behaviors,these and more are questions SPHERES can help a family and professionalcaregivers understand an independent living environment, makecomparisons to progress and decline, and which can inform discussionsand decisions for or with a loved one.

SPHERES may also include equipment and devices used to passively monitora home and/or occupants. A first and primary source monitored may beElectrical Use Monitoring System (such as one that may be tied into, ora part of, a home’s main breaker box) may be used to monitor time used,duration, what device is asking for power (e.g., appliance, furnace, hotwater heater, devices, garage door, overhead lights/lamp, alarm,electrical outlet, water pump, ignition source), and/or what isproviding external power (e.g., generator for emergencies). Othersources monitored may include smart phones, mobile devices, connecteddevices, connected assistants, connected appliances, connected homecontrols, connected safety systems, connected lighting or speakersystems, connected robotics or sensors, connected motion sensors,connected water sensors, connected gas and ignition monitors, connectedcontact sensors, connected air movement/draft sensors, connected petmonitoring, geo locator tags, weather monitor, connected vehiclesensors, Wi-Fi activity, medication dispensers or medical dispensersensors, 3D printers, nano-bots, fine motor control measurements, and/orsmart toilets/drains.

SPHERES may include sensors that may detect and measure video and stillpictures, audio and decibel levels, activity and movement, vibration,light, arm/disarm functionality, temperature (body and house), moisture,odor, heart rate, breathing rate, hydration, weight, glucose/ketoneslevels, medical adherence, travel and location, socialization,medical/health monitor use, appliance and electronics use, air quality,sleep, eye movement, exercise, body control, fine motor control, speech,health, nutrition, hygiene, and/or sight and hearing.

The data collected by SPHERES may be analyzed by one or more artificialintelligence or machine learning models, modules, algorithms, orprograms, including the types of machine learning techniques discussedelsewhere herein. For instance, the artificial intelligence or machinelearning models, modules, algorithms, or programs may be trained usingsample image, audio, home telematics, or other types of data, includingvideo and still images. Once trained, current or near real-time data,including video, image, audio, home telematics, or other data, may beinput into trained models, modules, algorithms, or programs to identifyabnormal or normal conditions or events, such as presence, walkingpatterns, falls, hazards, imminent danger, evidence of atypical behavior(physical, mental, emotional, social), intruders, theft, fraud, abuse,detecting position of furniture for safety layout recommendations,detecting trip and/or other hazards. Also detected may bemoisture/puddles on the floor or other surfaces or ceiling, and/or whenlights are on and off.

Audio and decibel conditions or events may also be identified ordetected by artificial intelligence programs or trained machine learningmodels, modules, programs, or algorithms, such as typical and atypicalvoice patterns (representing stoke, Alzheimer’s detection, hearingdecline - increased TV volume, cognitive decline - such as repeating),social (such as decline in conversation or detecting other people in thehouse), fall detection, behavioral change (such as more aggression,arguing or less conversation than normal), laughter, crying, othersounds relating to emotion, vehicle coming and going.

Activity and movement may also be identified or detected by artificialintelligence programs or trained machine learning models, modules,programs, or algorithms, such as moving from room to room, pace andspeed of movement, time spent in a space, time spent outside of thehouse, atypical movement (such as a seizure or movement before or aftera fall or injury), patterned walking style, movements and activitiesrelated to cooking, cleaning, exercise, entertainment, social, detectingmovements associated with a trip or stumble, and/or eye movement.

Activity and movement may also be identified or detected by artificialintelligence programs or trained machine learning models, modules,programs, or algorithms, such as detecting fall and impact level offall, detect an item drop/breaking, detecting entry/exit, detectingtornado, earthquake, or other disaster event, phone text/call, and/or avehicle coming going.

Light switch/light activation may also be identified or detected byartificial intelligence programs or trained machine learning models,modules, programs, or algorithms, such as detecting when lights turn onand off for individual locations, for what amount of time, and date ofactivation. The sensor or other data collected may also be used todetect activity of automatic lights that activate when movement or otherstimulus is detected.

Arming and disarming functionality may also be identified or detected byartificial intelligence programs or trained machine learning models,modules, programs, or algorithms. For instance, conditions that may beidentified or detected include entry/exit, alarm activation/disarm,alarm activation with no disarm, number of times over an amount of time,such as hour or single day a home security alarm is armed and disarmed.Also detected may be the amount of accidental alarm activations, and/orthe amount of times exiting a home with arming.

Home, home systems, and body temperature may also be identified ordetected by artificial intelligence programs or trained machine learningmodels, modules, programs, or algorithms. For instance, conditions thatmay be identified or detected include, home temperature highs, lows, andaverages; hot water temperature from heater, faucets, andbathtub/showers; oven or stovetop temperatures; body temperature ofinhabitants to understand if a notification is needed due to illness,accident, or death; differentiation of temperature between rooms of thehouse; and/or if vents or windows are open or closed.

Moisture may also be identified or detected by artificial intelligenceprograms or trained machine learning models, modules, programs, oralgorithms. For instance, conditions that may be identified or detectedinclude plumbing or other leaks, sump pump activation/issue, humidityaverages and out of average range in the home, and/or bed wetting oraccidents.

Odor may also be identified or detected by artificial intelligenceprograms or trained machine learning models, modules, programs, oralgorithms. For instance, conditions that may be identified or detectedmay include carbon monoxide/dioxide, air quality, smoke, stagnant air,mold/mildew, ammonia, body odor, feces, pet, urine, natural gas, burningfood, and/or the presence of certain foods which cause allergicreactions.

Medical/bio data may also be identified or detected by artificialintelligence programs or trained machine learning models, modules,programs, or algorithms. For instance, conditions that may be identifiedor detected may include medical information or characteristics, such asheart rate, BP, cholesterol, glucose, ketones, weight, hydration,nutrition, medication adherence or non-adherence, medical/health monitordevice use/adherence at home, and/or breathing rate.

Travel and location may also be identified or detected by artificialintelligence programs or trained machine learning models, modules,programs, or algorithms. For instance, GPS, travel itinerary, routinelocations, mode or service, purchases of travel, airline, train, orUber, length and time of travel, travel delays, interruptions, and/ordifficulties.

Socialization may also be identified or detected by artificialintelligence programs or trained machine learning models, modules,programs, or algorithms. For instance, conditions that may be identifiedor detected may include interaction routines, who interaction is with,how many times a day/week/month, and/or types/categories ofconversations.

Appliance and electronics use may also be identified or detected byartificial intelligence programs or trained machine learning models,modules, programs, or algorithms.

In the event that a condition, event, or abnormal condition is detectedor identified, a caregiver may receive a digital notification. Forinstance, digital messages may be received, such as via a mobile device,for fall, medical, emergency, or critical event, entry/exit, travel, ora list of caregiver selected events for anything outside of norms orrecommended norms in the above document.

Digital reports may be generated and transmitted to a caregiver’s mobileor other computing device. The digital reports may be easy to scan,read, print, share, and analyze Daily, Weekly, and Yearly snapshotreports; customized time period reports; and/or comparative timeframereports. The reports may detail the number of notifications sent tocaregiver and for what; and/or medication taken, for what, and if thatmedication is working. The reports may highlight areas of concern, areasof decline, and areas where intervention or improvement needs to takeplace or needs assistance or additional assistance. The reports maysuggest/recommend actions to be taken, and/or may feature recommendedservices, support, and resources within a certain proximity of thelocation. The reports may feature number ranges of optimal (based uponage/ability) levels of physical, mental, social, and emotionalengagement, activity, and ability and where recipients of care rank.

The reports may further suggest goals and goal setting/meeting featuresand/or provide an Independent Living overall score - as an example fordemonstration: 100 representing ideal independent living capability, 70representing family/support services interaction needed for independentliving capability, 60 representing significant family/support servicesinteraction needed to live independently, and 50 and below representingprofessional assistance needed. The reports may be able to be sharedwith doctors and selected family members but secured with a rotatinggenerated password provided by the designated primary family caregiveror Power of Attorney. Additionally or alternatively, the reports may beencrypted using blockchain or similar technology.

Additional Considerations

With the foregoing, an insurance customer may opt-in to a rewards,insurance discount, or other type of program. After the insurancecustomer provides their affirmative consent, an insurance providerremote server may collect data from the customer’s mobile device, smarthome controller, smart vehicles, computers, televisions, or other smartdevices - such as with the customer’s permission or affirmative consent.The data collected may be related to insured assets before (and/orafter) an insurance-related event, including those events discussedelsewhere herein. In return, risk averse insureds may receive discountsor insurance cost savings related to home, renters, personal articles,auto, and other types of insurance from the insurance provider.

In one aspect, data, including the types of data discussed elsewhereherein, may be collected or received by an insurance provider remoteserver, such as via direct or indirect wireless communication or datatransmission from a smart home controller, mobile device, or othercustomer computing device, after a customer affirmatively consents orotherwise opts-in to an insurance discount, reward, or other program.The insurance provider may then analyze the data received with thecustomer’s permission to provide benefits to the customer. As a result,risk averse customers may receive insurance discounts or other insurancecost savings based upon data that reflects low risk behavior and/ortechnology that mitigates or prevents risk to (i) insured assets, suchas homes, personal belongings, or vehicles, and/or (ii) home orapartment occupants.

Although the foregoing text sets forth a detailed description ofnumerous different embodiments, it should be understood that the legalscope of the invention may be defined by the words of the claims setforth at the end of this patent. The detailed description is to beconstrued as exemplary only and does not describe every possibleembodiment, as describing every possible embodiment would beimpractical, if not impossible. One could implement numerous alternateembodiments, using either current technology or technology developedafter the filing date of this patent, which would still fall within thescope of the claims.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Additionally, certain embodiments are described herein as includinglogic or a number of routines, subroutines, applications, orinstructions. These may constitute either software (e.g., code embodiedon a non-transitory, machine-readable medium) or hardware. In hardware,the routines, etc., are tangible units capable of performing certainoperations and may be configured or arranged in a certain manner. Inexample embodiments, one or more computer systems (e.g., a standalone,client or server computer system) or one or more hardware modules of acomputer system (e.g., a processor or a group of processors) may beconfigured by software (e.g., an application or application portion) asa hardware module that operates to perform certain operations asdescribed herein.

In various embodiments, a hardware module may be implementedmechanically or electronically. For example, a hardware module maycomprise dedicated circuitry or logic that may be permanently configured(e.g., as a special-purpose processor, such as a field programmable gatearray (FPGA) or an application-specific integrated circuit (ASIC)) toperform certain operations. A hardware module may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that may betemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement a hardware modulemechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations.

Accordingly, the term “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. Considering embodiments inwhich hardware modules are temporarily configured (e.g., programmed),each of the hardware modules need not be configured or instantiated atany one instance in time. For example, where the hardware modulescomprise a general-purpose processor configured using software, thegeneral-purpose processor may be configured as respective differenthardware modules at different times. Software may accordingly configurea processor, for example, to constitute a particular hardware module atone instance of time and to constitute a different hardware module at adifferent instance of time.

Hardware modules may provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multipleof such hardware modules exist contemporaneously, communications may beachieved through signal transmission (e.g., over appropriate circuitsand buses) that connect the hardware modules. In embodiments in whichmultiple hardware modules are configured or instantiated at differenttimes, communications between such hardware modules may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware modules have access. Forexample, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it may becommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and may operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processor-implemented. For example, at least some of theoperations of a method may be performed by one or more processors orprocessor-implemented hardware modules. The performance of certain ofthe operations may be distributed among the one or more processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor or processors maybe located in a single location (e.g., within an office environment, oras a server farm), while in other embodiments the processors may bedistributed across a number of locations.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment may be included in at leastone embodiment. The appearances of the phrase “in one embodiment” invarious places in the specification are not necessarily all referring tothe same embodiment.

As used herein, the terms “comprises,” “comprising,” “may include,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This is done merely forconvenience and to give a general sense of the description. Thisdescription, and the claims that follow, should be read to include oneor at least one and the singular also may include the plural unless itis obvious that it is meant otherwise.

This detailed description is to be construed as examples and does notdescribe every possible embodiment, as describing every possibleembodiment would be impractical, if not impossible. One could implementnumerous alternate embodiments, using either current technology ortechnology developed after the filing date of this application.

Unless a claim element is defined by reciting the word “means” and afunction without the recital of any structure, it is not intended thatthe scope of any claim element be interpreted based upon the applicationof 35 U.S.C. §112(f). The systems and methods described herein aredirected to an improvement to computer functionality, and improve thefunctioning of conventional computers.

What is claimed is:
 1. A computer-implemented method for training amachine learning module to identify abnormalities or anomaliescorresponding to historically identified conditions associated with oneor more individuals in a home environment, comprising: identifying, bythe processor, one or more abnormalities or anomalies in historicalsensor data detected by one or more sensors associated with the homeenvironment; analyzing, by the processor, using the machine learningmodule, the one or more abnormalities or anomalies in the historicalsensor data and historical condition data indicating historicallyidentified conditions associated with one or more individuals in thehome environment; and identifying, by the processor, using the machinelearning module, based upon the analysis, one or more abnormalities oranomalies in the historical sensor data corresponding to one or more ofthe historically identified conditions associated with the one or moreindividuals in the home environment.
 2. The computer-implemented methodof claim 1, further comprising: capturing current data detected by oneor more sensors associated with a home environment; and analyzing, bythe processor, the captured current data to identify one or moreabnormalities or anomalies in the current data using the machinelearning module.
 3. The computer-implemented method of claim 2, furthercomprising: comparing, by the processor, the one or more abnormalitiesor anomalies in the current data to the abnormalities or anomalies inthe historical sensor data corresponding to historically identifiedconditions associated with the one or more individuals in the homeenvironment; and determining, by the processor, based upon thecomparison, a current condition associated with an individual in thehome environment using the machine learning module.
 4. Thecomputer-implemented method of claim 3, further comprising: generating,by the processor, a notification indicating the current conditionassociated with the individual in the home environment.
 5. Thecomputer-implemented method of claim 3, wherein the current conditionassociated with the individual is a medical condition.
 6. Thecomputer-implemented method of claim 3, wherein the current conditionassociated with the individual is an emergency medical condition, themethod further comprising: requesting, by the processor, based upon theemergency medical condition, an emergency service to be provided to theindividual.
 7. The computer-implemented method of claim 3, wherein thecurrent condition associated with the individual is a gait of theindividual.
 8. The computer-implemented method of claim 3, wherein thecurrent condition associated with the individual is whether or not theindividual is sleeping.
 9. The computer-implemented method of claim 1,wherein the historical sensor data comprising at least one of a bodytemperature, a heart rate, a breathing rate, a glucose level, a ketonelevel, medication adherence data, eye movement data, exercise data, bodycontrol data, fine motor control data, health data, and nutrition data.10. The computer-implemented method of claim 1, further comprising:modifying, by the processor, the machine learning module based upon theanalysis and the identified one or more abnormalities or anomalies withcorresponding historically identified conditions.
 11. Thecomputer-implemented method of claim 1, wherein the one or more sensorsassociated with the home environment include one or more sensorsconfigured to capture data indicative of electricity use by devicesassociated with the home environment.
 12. The computer-implementedmethod of claim 11, wherein the data indicative of electricity useincludes an indication of at least one of: which device is usingelectricity; a time at which electricity is used by a particular device;a date at which electricity is used by a particular device; a durationof electricity use by a particular device; and a power source for theelectricity use.
 13. A computer system for training a machine learningmodule to identify abnormalities or anomalies corresponding tohistorically identified conditions associated with one or moreindividuals in a home environment, comprising: one or more processors;and one or more non-transitory memories storing computer executableinstructions that, when executed by the one or more processors, causethe computer system to: identify one or more abnormalities or anomaliesin historical sensor data detected by one or more sensors associatedwith the home environment; analyze, using the machine learning module,the one or more abnormalities or anomalies in the historical sensor dataand historical condition data indicating historically identifiedconditions associated with one or more individuals in the homeenvironment; and identify, using the machine learning module, based uponthe analysis, one or more abnormalities or anomalies in the historicalsensor data corresponding to one or more of the historically identifiedconditions associated with the one or more individuals in the homeenvironment.
 14. The computer system of claim 13, wherein the computerexecutable instructions further cause the computer system to: capturecurrent data detected by one or more sensors associated with a homeenvironment; and analyze the captured current data to identify one ormore abnormalities or anomalies in the current data using the machinelearning module.
 15. The computer system of claim 14, wherein thecomputer executable instructions further cause the computer system to:compare the one or more abnormalities or anomalies in the current datato the abnormalities or anomalies in the historical sensor datacorresponding to historically identified conditions associated with theone or more individuals in the home environment; and determine, basedupon the comparison, a current condition associated with an individualin the home environment using the machine learning module.
 16. Thecomputer system of claim 15, wherein the computer executableinstructions further cause the computer system to: generate anotification indicating the current condition associated with theindividual in the home environment.
 17. The computer system of claim 15,wherein the current condition associated with the individual is amedical condition.
 18. The computer system of claim 15, wherein thecurrent condition associated with the individual is an emergency medicalcondition, wherein the computer executable instructions further causethe computer system to: request, based upon the emergency medicalcondition, an emergency service to be provided to the individual. 19.The computer system of claim 13, wherein the one or more sensorsassociated with the home environment include one or more sensorsconfigured to capture data indicative of electricity use by devicesassociated with the home environment.
 20. The computer system of claim19, wherein the data indicative of electricity use includes anindication of at least one of: which device is using electricity; a timeat which electricity is used by a particular device; a date at whichelectricity is used by a particular device; a duration of electricityuse by a particular device; and a power source for the electricity use.21. The computer system of claim 13, wherein the historical sensor datacomprising at least one of a body temperature, a heart rate, a breathingrate, a glucose level, a ketone level, medication adherence data, eyemovement data, exercise data, body control data, fine motor controldata, health data, and nutrition data.
 22. The computer system of claim13, wherein the computer executable instructions further cause thecomputer system to: modify the machine learning module based upon theanalysis and the identified one or more abnormalities or anomalies withthe corresponding historically identified conditions.
 23. The computersystem of claim 13, wherein the computer executable instructions furthercause the computer system to interact with a voice-activated assistant.